Vampire 1.1 (System Description)
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
The complexity of belief update
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On the partial observability of temporal uncertainty
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Monte Carlo tree search techniques in the game of Kriegspiel
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Solving satisfiability in ground logic with equality by efficient conversion to propositional logic
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Monte Carlo tree search in Kriegspiel
Artificial Intelligence
Playing the perfect Kriegspiel endgame
Theoretical Computer Science
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Artificial Intelligence
Solving kriegspiel endings with brute force: the case of KR vs. k
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
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Partially observed actions are observations of action executions in which we are uncertain about the identity of objects, agents, or locations involved in the actions (e.g., we know that action move(?o, ?x, ?y) occurred, but do not know ?o, ?y). Observed-Action Reasoning is the problem of reasoning about the world state after a sequence of partial observations of actions and states. In this paper we formalize Observed-Action Reasoning, prove intractability results for current techniques, and find tractable algorithms for STRIPS and other actions. Our new algorithms update a representation of all possible world states (the belief state) in logic using new logical constants for unknown objects. A straightforward application of this idea is incorrect, and we identify and add two key amendments. We also present successful experimental results for our algorithm in Blocks-world domains of varying sizes and in Kriegspiel (partially observable chess). These results are promising for relating sensors with symbols, partial-knowledge games, multi-agent decision making, and AI planning.